Skip to main content

Exploring Depth Versus Breadth in Knowledge Management Strategies

Computational & Mathematical Organization Theory volume 8, pages 49–73 (2002)Cite this article

Abstract

This paper provides an early attempt at operationalizing and testing the concept of knowledge strategy. Using a computer-simulated product development process, we compare the performance of generalist and specialist knowledge management strategies under conditions of market turbulence. The generalist knowledge strategy emphasizes breadth of knowledge in product development teams, while the specialist strategy focuses on depth of knowledge. Our generalist and specialist strategies are grounded in Eastern and Western perspectives of knowledge management, respectively. A primary difference between these two approaches is the strong emphasis on cross-learning, or learning across team members, in the Eastern perspective relative to the Western perspective. As such, we examine the performance implications of different modes of cross-learning for teams utilizing the generalist knowledge strategy. Specifically, we examine three modes of cross-learning, which are reflected in the use of three learning decision rules: (1) averaging, (2) majority, and (3) hot hand. A learning decision rule indicates how decision-makers learn from their fellow team members. Under the first rule, the decision-maker adopts an average of the beliefs held by fellow team members. Under the second rule, if a majority of fellow team members agree on a particular solution, then the decision-maker adopts the beliefs held by the majority. Under the third rule, the decision-maker learns from the team member whose beliefs have been consistent with market desires most recently. Surprisingly, we find that specialist strategies outperform generalist strategies under conditions of low and high market turbulence. We also find that cross-learning can be beneficial or detrimental, contingent upon the mode of learning. Generalist teams utilizing the averaging decision rule perform significantly worse, while generalist teams utilizing the hot hand decision rule perform significantly better.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price includes VAT (USA)
Tax calculation will be finalised during checkout.

References

  • Ashby, W.R. (1956), An Introduction to Cybernetics. Wiley, New York.

    Google Scholar 

  • Axelrod, R. (1997), “Advancing the Art of Simulation in the Social Sciences,” Presented at the International Conference on Computer Simulation and the Social Sciences (Cortona, Italy, September 22–25).

  • Baldwin, C.Y. and K.B. Clark (2000), Design Rules: The Power of Modularity, Vol. 1. MIT Press, Cambridge.

    Google Scholar 

  • Baum, J.A.C. (1999), “Organizational Ecology,” in S. R. Clegg and C. Hardy (Eds.) Studying Organization, Sage Publications, London, pp. 71–108.

    Google Scholar 

  • Besen, S.M. and J. Farrell (1994), “Choosing howto Compete—Strategies and Tactics in Standardization,” Journal of Economic Perspectives, 8(2), 117–131.

    Google Scholar 

  • Bower, G.H. and E.R. Hilgard (1981), Theories of Learning. Prentice-Hall, Englewood Cliffs, NJ.

    Google Scholar 

  • Brown, S.L. and K.M. Eisenhardt (1997), “The Art of Continuous Change: Linking Complexity Theory and Time-Paced Evolution in Relentlessly Shifting Organizations,” Administrative Science Quarterly, 42(1), 1–34.

    Google Scholar 

  • Burns, T. and G.M. Stalker (1961), The Management of Innovation. Tavistock Publications Limited, London.

    Google Scholar 

  • Burton, R.M. and B. Obel (1995), “The Validity of Computational Models in Organization Science: From Model Realism to Purpose of the Model,” Computational and Mathematical Organization Theory, 1(1), 57–71.

    Google Scholar 

  • Carley, K. (1992), “Organizational Learning and Personnel Turnover,” Organization Science, 3, 20–46.

    Google Scholar 

  • Carley, K.M. (1995), “Computational and Mathematical Organization Theory: Perspectives and Directions,” Computational and Mathematical Organization Theory, 1(1), 39–56.

    Google Scholar 

  • Carley, K.M. and J. Lee (1998), “Dynamic Organizations: Organizational Adaptation in a Changing Environment” in J. Baum (Ed.) Advances in Strategic Management, Vol. 15, JAI Press, London, pp. 269–297.

    Google Scholar 

  • Carley, K.M. and Z. Lin (1997), “A Theoretical Study of Organizational Performance Under Information Distortion,” Management Science, 43(7), 976–997.

    Google Scholar 

  • Christensen, C.M. (1992), The Innovator's Challenge: Understanding the Influence of Market Environment on Processes of Technology Development in the Rigid Disk Drive Industry, DBA Dissertation, Graduate School of Business Administration, Harvard University.

  • Christensen, C.M. (1997), The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail. Harvard Business School Press, Boston.

    Google Scholar 

  • Cohen, D. (1998), “Toward a Knowledge Context: Report on the First Annual U. C. Berkeley Forum on Knowledge and the Firm,” California Management Review, 40, 22–39.

    Google Scholar 

  • Daft, R. and K. Weick (1984), “Toward a Model of Organizations as Interpretation Systems,” Academy of Management Review, 9(2), 284–295.

    Google Scholar 

  • Donaldson, L. (1999), “The Normal Science of Structural Contingency Theory,” in S.R. Clegg and C. Hardy (Eds.) Studying Organization, Sage Publications, London, pp. 51–70.

    Google Scholar 

  • Dougherty, D. (1992), “Interpretive Barriers to Successful Product Innovation in Large Firms,” Organization Science, 3(2), 179–202.

    Google Scholar 

  • Gersick, C.J.G. (1988), “Time and Transition in Work Teams: Toward a New Model of Group Development,” Academy of Management Journal, 31(1), 9–41.

    Google Scholar 

  • Gersick, C.J.G. (1991), “Revolutionary Change Theories:AMulti-Level Exploration of the Punctuated Equilibrium Paradigm,” Academy of Management Review, 16(1), 10–36.

    Google Scholar 

  • Gigone, D. and R. Hastie (1993), “The Common Knowledge Effect: Information Sharing and Group Judgment,” Journal of Personality and Social Psychology, 65, 959–974.

    Google Scholar 

  • Gilovich, T., R. Vallone and A. Tversky (1985), “The Hot Hand in Basketball: On the Misperception of Random Sequences,” Cognitive Psychology, 17, 295–314.

    Google Scholar 

  • Grant, R.M. (1996), “Toward a Knowledge-Based Theory of the Firm,” Strategic Management Journal, 17, 109–122.

    Google Scholar 

  • Griffin, A. and J.R. Hauser (1996), “Integrating R&D and Marketing: A Review and Analysis of the Literature,” Journal of Product Innovation Management, 13, 191–215.

    Google Scholar 

  • Hambrick, D.C. (1983), “High Profit Strategies in Mature Capital Goods Industries: A Contingency Approach,” Academy of Management Journal, 26(4), 687–707.

    Google Scholar 

  • Hannan, M.T. and J. Freeman (1977), “The Population Ecology of Organizations,” American Journal of Sociology, 82, 929–964.

    Google Scholar 

  • Hannan, M.T. and J. Freeman (1989), Organizational Ecology. Harvard University Press, Cambridge.

    Google Scholar 

  • Holloman, C.R. and H.W. Hendrick (1972), “Adequacy of Group Decisions as a Function of the Decision-Making Process,” Academy of Management Journal, 15(2), 175–184.

    Google Scholar 

  • Iansiti, M. (1993), “Real-world R&D: Jumping the Product Generation Gap,” Harvard Business Review, 71(3), 138–147.

    Google Scholar 

  • Ilgen, D.R., D.A. Major, J.R. Hollenbeck and D.J. Sego (1995), “Raising an Individual Decision-Making Model to the Team Level: A New Research Model and Paradigm,” in R.A. Guzzo, E. Salas and Associates (Eds.) Team Effectiveness and Decision Making in Organizations, Josey-Bass Publishers, San Francisco, pp. 113–148.

    Google Scholar 

  • Imai, K., I. Nonaka and H. Takeuchi (1985), “Managing the New Product Development Process: How Japanese Companies Learn and Unlearn,” in K.B. Clark, R.H. Hayes and C. Lorenz (Eds.) The Uneasy Alliance: Managing the Productivity-Technology Dilemma, Harvard Business School Press, Boston, pp. 337–375.

    Google Scholar 

  • Lawrence, P.R. and J.W. Lorsch (1967), Organization and Environment. Division of Research, Graduate School of Business Administration, Harvard University, Boston.

    Google Scholar 

  • Leonard, D. (1995), Wellsprings of Knowledge. Harvard Business School Press, Boston.

    Google Scholar 

  • Levinthal, D. and J.G. March (1981), “AModel of Adaptive Organizational Search,” Journal of Economic Behavior and Organization, 2(4), 307–333.

    Google Scholar 

  • Levinthal, D.A. and J.G. March (1993), “The Myopia of Learning,” Strategic Management Journal, 14, 95–112.

    Google Scholar 

  • Lin, Z. and K.M. Carley (1997), “Organizational Response: The Cost PerformanceTradeoff,”Management Science, 43(2), 217–234.

    Google Scholar 

  • Madhavan, R. and R. Grover (1998), “From Embedded Knowledge to Embodied Knowledge: New Product Development as Knowledge Management,” Journal of Marketing, 62, 1–12.

    Google Scholar 

  • March, J.G. (1991), “Exploration and Exploitation in Organizational Learning,” Organization Science, 2, 71–87.

    Google Scholar 

  • March, J.G. and H. Simon (1958), Organizations. Wiley, New York.

    Google Scholar 

  • McGrath, J.E. (1982), “Dilemmatics: The Study of Research Choices and Dilemmas,” in J.E. McGrath, J. Martin and R.A. Kulka (Eds.) Judgment Calls in Research, Sage Publications, Beverly Hills, pp. 69–102.

    Google Scholar 

  • Miller, J.H. (1998), “Active Nonlinear Tests (ANTs) of Complex Simulation Models,” Management Science, 44(6), 820–830.

    Google Scholar 

  • Nonaka, I. (1990), “Redundant, Overlapping Organization: A Japanese Approach to Managing the Innovation Process,” California Management Review, 32(3), 27–38.

    Google Scholar 

  • Nonaka, I. and H. Takeuchi (1995), The Knowledge-Creating Company. Oxford University Press, New York.

    Google Scholar 

  • Norman, P.M. and R.A. Bettis (1997), “Technological Complexity and the New Architecture of Competition,” in H. Thomas, D. O'Neal and R. Alvarado (Eds.) Strategic Discovery: Competing in New Arenas, JohnWiley and Sons, New York, pp. 5–22.

    Google Scholar 

  • Rulke, D.L. and J. Galaskiewicz (2000), “Distribution of Knowledge, Group Network Structure, and Group Performance,” Management Science, 46(5), 612–625.

    Google Scholar 

  • Sanchez, R. and J.T. Mahoney (1996), “Modularity, Flexibility, and Knowledge Management in Product and Organization Design,” Strategic Management Journal, 17, 63–76.

    Google Scholar 

  • Sastry, M.A. (1997), “Problems and Paradoxes in a Model of Punctuated Organizational Change,” Administrative Science Quarterly, 42, 237–275.

    Google Scholar 

  • Shukla, R.K. (1982), “Influence of Power Bases in Organizational Decision Making: A Contingency Model,” Decision Sciences, 13(3), 450–470.

    Google Scholar 

  • Simon, H.A. (1997), Administrative Behavior. Free Press, New York.

    Google Scholar 

  • Spender, J.-C. and R.M. Grant (1996), “Knowledge and the Firm: Overview,” Strategic Management Journal, 17, 5–9.

    Google Scholar 

  • Sun Tzu (1963), The Art of War. translation by S.B. Griffith, Oxford University Press, London.

    Google Scholar 

  • von Hippel, E. (1990), “Task Partitioning: An Innovation Process Variable,” Research Policy, 19, 407–418.

    Google Scholar 

  • Williamson, O.E. (1975), Markets and Hierarchies. Free Press, New York.

    Google Scholar 

  • Wittenbaum, G.M. and G. Stasser (1996), “Management of Information in Small Groups,” in J.L. Nye and A.M. Brower (Eds.) What's Social About Social Cognition?, Sage Publications, Thousand Oaks, pp. 3–28.

    Google Scholar 

  • Wooldridge, B. and S.W. Floyd (1990), “The Strategy Process, Middle Management Involvement, and Organizational Performance,” Strategic Management Journal, 11(3), 231–241.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kenan-Flagler Business School, University of North Carolina at Chapel Hill, Campus Box 3490, McColl Building, Chapel Hill, NC, 27599-3490, USA

    Scott F. Turner & Richard A. Bettis

  2. Fuqua School of Business, Duke University, Box 90120, Durham, NC, 27708, USA

    Richard M. Burton

Authors
  1. Scott F. Turner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Richard A. Bettis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Richard M. Burton
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Turner, S.F., Bettis, R.A. & Burton, R.M. Exploring Depth Versus Breadth in Knowledge Management Strategies. Computational & Mathematical Organization Theory 8, 49–73 (2002). https://doi.org/10.1023/A:1015180220717

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1015180220717

  • knowledge management
  • product development
  • organizational learning
  • computer simulation
  • decision-making
  • specialist
  • generalist